A Cursory Introduction to Byzantine Fault Tolerance and Alternative Consensus

What can we expect from distributed systems?

Alexandra Tran
Jul 25, 2017 · 9 min read

Update: an alternative approach of mine to presenting the following information can be found here.

If you are involved in the blockchain space, you may have heard of a genre of consensus mechanisms known as “BFT-based proof-of-stake”, exemplified by algorithms like Tendermint and Casper. To build up an understanding of what that could possibly entail, we must start by analyzing the theoretical foundation of consensus algorithms in blockchain systems, which is traditional Byzantine fault tolerance research.

Byzantine Fault Tolerance

So, recall that a blockchain requires a mechanism to reach distributed consensus, or validate and canonicalize a single chain. Byzantine fault tolerance refers to the characteristic of distributed systems that allows them to reach consensus in the face of Byzantine faults, i.e. in situations where components of the system will fail — but not just fail-stop — Byzantine-faulty nodes will act arbitrarily and often present conflicting information to different nodes in the system.

It gets its name from the Byzantine Generals Problem, the scenario in which the Byzantine army has multiple separate divisions surrounding an enemy city, and they have to all agree on a common plan to attack or retreat. All, or at least nearly all, must perform the same action or they will risk complete failure. The catch is, the generals of each division can only communicate with each other individually via messenger, and there may be traitorous generals who send different information to different generals.

Non-rigorous proof of Lamport-Shostak-Pease result.

It has been proven in the original Byzantine Generals Problem paper by Lamport, Shostak, and Pease, that no solution with fewer than 3m + 1 generals can cope with m traitors; i.e., consensus is impossible to achieve if ⅓ or more of the generals are traitors.

There is an isomorphism between a typical blockchain network, which is a distributed system, and the Byzantine generals scenario:

A solution to the problem Bitcoin and Ethereum currently implement is “proof-of-work”, which requires consumption of a resource external to the system, hashing power, to solve “difficulty” problems in order to win the ability to create (transaction-filled) blocks. This allows the respective system to reach consensus on the chain (of blocks) with the largest combined difficulty, i.e. the longest chain.

Correctness of Distributed Systems

Let’s take a step back and look at “correctness” properties of distributed systems, like the Byzantine army or the Bitcoin network. As shown in “Proving the Correctness of Multiprocess Programs” by Leslie Lamport (1977), the correctness of distributed (i.e. concurrent, i.e. multiprocess) systems can be described in terms of liveness and safety. Both properties must be satisfied for the system to achieve a strict and ideal sense of correctness — and it’s often difficult to internalize where the differences between the two properties lie.

A liveness property is a guarantee that something good will eventually happen. Examples include: a guarantee that a computation will eventually terminate; that the customer will eventually be served; that some candidate will eventually be elected to a political office. In a system where the goal is consensus, a liveness property would be a guarantee that each component will eventually decide on a value (this can be referred to as termination).

A safety property is a guarantee that something bad will never happen. Examples include: a guarantee that a system will never deadlock; that an innocent person will never be convicted of a crime. In consensus, a safety property would be a guarantee that different components will never decide on different values (this can be referred to as agreement).

FLP Impossibility

The next development in distributed system correctness is the FLP impossibility, named after Fischer, Lynch, and Paterson, who described this result in their 1985 paper “Impossibility of Distributed Consensus with One Faulty Process”.

The FLP impossibility states that both termination and agreement (liveness and safety) cannot be satisfied in a timebound manner in an asynchronous distributed system, if it is to be resilient to at least one fault (they prove their result for general fault tolerance, which is weaker than Byzantine fault tolerance, since it only requires one fail-stop node — so BFT is included inside FLP impossibility claims).

FLP impossibility states that asynchronous fault tolerant systems cannot simultaneously satisfy liveness and safety — thus, distributed systems cannot achieve fault tolerance AND correctness.

In asynchronous systems, there is no upper bound on the amount of time components (nodes, generals) may take to receive, process and respond to messages. Note that synchronous systems can be made fault tolerant, and Byzantine fault tolerant with the usual <⅓ faulty nodes. Assuming synchronicity of a network is a very strong, unrealistic assumption for, say, Bitcoin, which is designed to work on a network with message delays.

CAP Theorem

Another related development is the CAP theorem by Eric Brewer (2000). It states something similar to the FLP impossibility — that you cannot have quintessential properties of consistency, availability, and partition tolerance all at once in an asynchronous distributed database. At most, two of the three properties can be satisfied.

Consistency is a guarantee that reading from each node will return the correct (most recent) write. (guarantee that nothing bad will ever happen)

Availability is a guarantee that reading from any node will return some response. (guarantee that something good will eventually happen)

Partition tolerance is a guarantee that the system will still operate in the face of a network partition, across which some messages between nodes cannot be delivered (fault tolerance)

Given a partitioned network, there will be a tradeoff between consistency and availability.

Here’s a simple example of CAP theorem in action. Given a partitioned network there will be a tradeoff between consistency and availability. If I’m the yellow node, and I have $100, I tell each of the gray nodes to send the same $100 to different addresses. The system either won’t be consistent, because each half sees a different transaction, or it won’t be available, because only one or neither transaction will be canonicalized. (This is looking like our Byzantine generals problem. Note that partition tolerance doesn’t necessarily entail the existence of Byzantine faults.)


By CAP Theorem, fault tolerant (partitioned network) consensus (availability and consistency) is unachievable in asynchronous systems. By FLP impossibility, fault tolerant (single fault) consensus (termination and agreement) is unachievable in asynchronous systems.

FLP impossibility seems to be the superset of CAP Theorem, since it makes impossibility claims about weaker systems — FLP-admissible systems need only one faulty node, and messages can be delayed but not dropped (there is no network partition). CAP Theorem, thus, appears to be a particular instance of FLP impossibility, and both are examples of the fundamental fact that you cannot achieve both liveness and safety in an unreliable distributed system.

List of Early BFT Solutions

Some early solutions to Byzantine faults include the family of consensus protocols known as Paxos (1989)— it uses the common fault tolerant method of state machine replication involving several roles, a quorum rule, and two “promise” and “accept” phases in which nodes communicate to learn an agreed value. “Byzantine Paxos” is an extension of Paxos that accounts for Byzantine faults. Safety (consistency) is guaranteed — as is the case with any state machine replication, where the nodes must reach agreement to come to consensus. By FLP impossibility, liveness is not guaranteed. Raft (2013) is meant to be a simplified, easy-to-understand version of Paxos.

Practical Byzantine Fault Tolerance (1999) is another state machine replication approach to fault tolerance, also containing a quorum rule and a multi-phase learning process. It guarantees both liveness and safety, but it relies on a weak form of synchrony.

Nakamoto Consensus

To further apply what we know about safety and liveness, we’re going to take a look at some existing consensus mechanisms that blockchains use. “Nakamoto consensus”, as it is colloquially called, refers to Satoshi Nakamoto’s original proof-of-work consensus, in which the first announced valid block containing a solution to a predetermined computational puzzle is considered correct, and the longest chain is considered the canonical chain. In PoW, simultaneously found solutions and malicious mining lead to forks. Thus, it favors liveness (availability) over safety (consistency).

Nakamoto consensus can be generalized to refer to “chain-based” consensus at large, in which the protocol states that you must choose among several possible chains. Thus, when proof-of-stake initially became a popular alternative to proof-of-work, because it doesn’t require wastage of the vast computing power that PoW does, the first proof-of-stake consensus algorithms were chain-based. In Nakamoto-based proof-of-stake, the consensus algorithm pseudo-randomly selects a stakeholder (in proportion to their stake) and assigns them the right to create a single block. It is, in a sense, “simulated PoW”. Again, network delays and deviant actors will lead to inevitable forks. Thus, Nakamoto-based proof-of-stake favors liveness (availability) over safety (consistency).

What many people began to realize was that Nakamoto consensus was necessary for proof-of-work, but not proof-of-stake. Since Nakamoto PoS, in its naive form, suffers from the “nothing at stake” problem and long-range attacks, people sought a new way to achieve PoS without relying on chain selection. In comes BFT-based proof-of-stake, or, technically, state-machine-replication-based proof-of-stake — which was an old innovation!

BFT-based proof-of-stake favors safety (consistency) over liveness (availability). Can you deduce why?

List of Nakamoto-based Proof-of-Stake Examples

  • Peercoin (2012): Peercoin is the first known implementation of a hybrid proof-of-stake/proof-of-work cryptocurrency. It uses coin age consumed by a transaction as proof of stake as well as centrally broadcasted “checkpoints”.
  • NXT (2014): NXT uses an account’s effective balance, not coin age, as part of its block creation algorithm.
  • Slasher (2014): Slasher is Vitalik’s punitive PoS algorithm which uses security deposits and “weak subjectivity” to combat the nothing at stake problem and long range attacks.
  • Bitshares (circa 2015): Bitshares uses “delegated-proof-of-stake”. It is a “technological democracy”, in which stakeholders vote (via transactions) on trusted delegates to sign blocks.
  • Tezos (2014): Tezos uses a mix of Slasher, “chain-of-activity”, and “proof-of-burn”.
  • Ouroboros (2017): Ouroboros is “a provably secure proof-of-stake blockchain protocol”; it is chain-based and uses synchronous settings.
Peercoin, NXT, Bitshares, and Tezos logos

List BFT-based and “Other” Proof-of-Stake Examples

  • Tendermint (2014): Tendermint is a mostly asynchronous, BFT consensus protocol with voting power denominated in validator stake; it has a two-stage voting process; it favors safety (consistency). Tendermint is used in the heterogeneous blockchain network Cosmos.
  • Casper (2015): Casper is Ethereum’s to-be-released Nakamoto/BFT hybrid proof-of-stake algorithm; it favors liveness (availability); it is named after the friendly “GHOST”, as it incorporates properties of the GHOST (Greedy Heaviest Observed Sub-Tree) protocol.
  • Algorand (2017): Algorand’s proof-of-stake is based on (a novel and fast) message-passing Byzantine agreement. It is neither hybrid nor fully Nakamoto/state machine replication. Feel free to tackle the whitepaper and explain it to me (I can also refer those interested to study groups reading this whitepaper).
Tendermint, Cosmos, and Ethereum logos

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